Traffic Emission Estimation and Modelling at Urban Intersections Using High-Resolution UAV-Based Video

Swiftly assessing emissions at urban traffic intersections without installed monitoring devices poses a challenge. Traditional methods of collecting vehicle emission data are constrained by costs or limitations in traffic road infrastructure, lacking a cost-effective approach. To address this challenge, this paper proposes a method for estimating vehicle emissions at intersections based on UAV videos by taking advantage of the fact that UAVs are easy to deploy, low-cost, and unaffected by ground traffic conditions. This method enables the quick estimation of greenhouse gas and air pollutant emissions, such as CO
2
, NO
x
, and PM
10
emissions at intersections. Initially, the YOLO-SD algorithm is employed to extract accurate vehicle trajectories from UAV videos. Subsequently, this data is input into the VERSIT+ micro emission model to estimate vehicle emissions. Subsequently, based on the actual traffic condition at the intersection, a reliable microscopic traffic simulation model is developed using VISSIM to estimate vehicle emissions. Comparative analysis of the drone-based intersection emission estimation results and the high-fidelity intersection model results reveal a relative error of 9% for CO
2
emissions, indicating good consistency of the models. However, noticeable differences exist in NO
x
and PM
10
emissions. Through experimental result comparisons, this study validates the feasibility and accuracy of the proposed method based on drone video for estimating vehicular emissions at intersections, particularly in estimating CO
2
at intersections.